Overview

Dataset statistics

Number of variables19
Number of observations57851
Missing cells4853
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 MiB
Average record size in memory152.0 B

Variable types

Text2
DateTime1
Numeric10
Categorical5
Unsupported1

Alerts

Category is highly overall correlated with SubcategoryHigh correlation
Color is highly overall correlated with SubcategoryHigh correlation
Continent is highly overall correlated with CountryHigh correlation
Cost is highly overall correlated with Profit_Margin and 2 other fieldsHigh correlation
Country is highly overall correlated with Continent and 2 other fieldsHigh correlation
EmployeeKey is highly overall correlated with Country and 1 other fieldsHigh correlation
ProductKey is highly overall correlated with SubcategoryHigh correlation
Profit is highly overall correlated with Profit_MarginHigh correlation
Profit_Margin is highly overall correlated with Cost and 1 other fieldsHigh correlation
Sales is highly overall correlated with Cost and 1 other fieldsHigh correlation
SalesTerritoryKey is highly overall correlated with Country and 1 other fieldsHigh correlation
Subcategory is highly overall correlated with Category and 2 other fieldsHigh correlation
Unit Price is highly overall correlated with Cost and 1 other fieldsHigh correlation
Continent is highly imbalanced (52.3%)Imbalance
Color has 4853 (8.4%) missing valuesMissing
Profit_Margin is highly skewed (γ1 = -39.79601447)Skewed
trimestre is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-10 00:35:10.882269
Analysis finished2024-04-10 00:35:58.241513
Duration47.36 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct3616
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Memory size452.1 KiB
2024-04-10T00:35:58.665075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters404957
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique564 ?
Unique (%)1.0%

Sample

1st rowSO43897
2nd rowSO43897
3rd rowSO43897
4th rowSO43897
5th rowSO44544
ValueCountFrequency (%)
so51721 72
 
0.1%
so51739 72
 
0.1%
so51160 71
 
0.1%
so53465 71
 
0.1%
so47355 68
 
0.1%
so57046 67
 
0.1%
so51120 67
 
0.1%
so51090 66
 
0.1%
so55297 66
 
0.1%
so47395 66
 
0.1%
Other values (3606) 57165
98.8%
2024-04-10T00:35:59.681723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 57851
14.3%
O 57851
14.3%
4 45461
11.2%
5 45205
11.2%
6 32266
8.0%
7 27168
6.7%
1 25806
6.4%
3 24595
6.1%
0 23661
5.8%
9 23228
5.7%
Other values (2) 41865
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 404957
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 57851
14.3%
O 57851
14.3%
4 45461
11.2%
5 45205
11.2%
6 32266
8.0%
7 27168
6.7%
1 25806
6.4%
3 24595
6.1%
0 23661
5.8%
9 23228
5.7%
Other values (2) 41865
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 404957
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 57851
14.3%
O 57851
14.3%
4 45461
11.2%
5 45205
11.2%
6 32266
8.0%
7 27168
6.7%
1 25806
6.4%
3 24595
6.1%
0 23661
5.8%
9 23228
5.7%
Other values (2) 41865
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 404957
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 57851
14.3%
O 57851
14.3%
4 45461
11.2%
5 45205
11.2%
6 32266
8.0%
7 27168
6.7%
1 25806
6.4%
3 24595
6.1%
0 23661
5.8%
9 23228
5.7%
Other values (2) 41865
10.3%
Distinct990
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size452.1 KiB
Minimum2017-07-01 00:00:00
Maximum2020-05-31 00:00:00
2024-04-10T00:36:00.206460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:36:00.718175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ProductKey
Real number (ℝ)

HIGH CORRELATION 

Distinct334
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.6367
Minimum212
Maximum606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:01.184687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum212
5-th percentile223
Q1326
median401
Q3491
95-th percentile586
Maximum606
Range394
Interquartile range (IQR)165

Descriptive statistics

Standard deviation113.66564
Coefficient of variation (CV)0.27815819
Kurtosis-1.058689
Mean408.6367
Median Absolute Deviation (MAD)82
Skewness-0.012319591
Sum23640042
Variance12919.879
MonotonicityNot monotonic
2024-04-10T00:36:01.683806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
471 466
 
0.8%
491 447
 
0.8%
224 442
 
0.8%
458 437
 
0.8%
233 436
 
0.8%
456 418
 
0.7%
483 418
 
0.7%
225 417
 
0.7%
234 410
 
0.7%
477 402
 
0.7%
Other values (324) 53558
92.6%
ValueCountFrequency (%)
212 200
0.3%
213 317
0.5%
214 306
0.5%
215 201
0.3%
216 340
0.6%
217 348
0.6%
218 188
0.3%
219 44
 
0.1%
220 215
0.4%
221 372
0.6%
ValueCountFrequency (%)
606 286
0.5%
605 298
0.5%
604 98
 
0.2%
603 188
0.3%
601 125
0.2%
600 95
 
0.2%
599 161
0.3%
598 123
0.2%
597 138
0.2%
596 91
 
0.2%

ResellerKey
Real number (ℝ)

Distinct632
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean340.74298
Minimum1
Maximum701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:02.045268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q1166
median327
Q3514
95-th percentile675
Maximum701
Range700
Interquartile range (IQR)348

Descriptive statistics

Standard deviation205.49391
Coefficient of variation (CV)0.60307598
Kurtosis-1.2241433
Mean340.74298
Median Absolute Deviation (MAD)175
Skewness0.084246705
Sum19712322
Variance42227.745
MonotonicityNot monotonic
2024-04-10T00:36:02.641299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
678 469
 
0.8%
514 451
 
0.8%
54 443
 
0.8%
496 441
 
0.8%
175 440
 
0.8%
328 436
 
0.8%
84 436
 
0.8%
221 432
 
0.7%
167 422
 
0.7%
475 418
 
0.7%
Other values (622) 53463
92.4%
ValueCountFrequency (%)
1 73
 
0.1%
2 70
 
0.1%
3 359
0.6%
4 235
0.4%
5 71
 
0.1%
6 5
 
< 0.1%
7 16
 
< 0.1%
8 31
 
0.1%
9 30
 
0.1%
10 217
0.4%
ValueCountFrequency (%)
701 11
 
< 0.1%
700 157
0.3%
699 1
 
< 0.1%
698 21
 
< 0.1%
697 298
0.5%
696 2
 
< 0.1%
695 60
 
0.1%
694 8
 
< 0.1%
693 89
 
0.2%
692 82
 
0.1%

EmployeeKey
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean286.13173
Minimum272
Maximum296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:02.911476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum272
5-th percentile281
Q1283
median285
Q3290
95-th percentile294
Maximum296
Range24
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.5218323
Coefficient of variation (CV)0.015803323
Kurtosis-0.073423976
Mean286.13173
Median Absolute Deviation (MAD)3
Skewness0.12307418
Sum16553007
Variance20.446967
MonotonicityNot monotonic
2024-04-10T00:36:03.145772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
283 7529
13.0%
282 6843
11.8%
281 6734
11.6%
291 6374
11.0%
285 5250
9.1%
287 4357
7.5%
288 4159
7.2%
292 3311
5.7%
284 3138
 
5.4%
289 2146
 
3.7%
Other values (7) 8010
13.8%
ValueCountFrequency (%)
272 747
 
1.3%
281 6734
11.6%
282 6843
11.8%
283 7529
13.0%
284 3138
5.4%
285 5250
9.1%
286 1945
 
3.4%
287 4357
7.5%
288 4159
7.2%
289 2146
 
3.7%
ValueCountFrequency (%)
296 1183
 
2.0%
295 1561
 
2.7%
294 240
 
0.4%
293 1750
 
3.0%
292 3311
5.7%
291 6374
11.0%
290 584
 
1.0%
289 2146
 
3.7%
288 4159
7.2%
287 4357
7.5%

SalesTerritoryKey
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5544243
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:03.376330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q36
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4122469
Coefficient of variation (CV)0.52964914
Kurtosis-0.30710651
Mean4.5544243
Median Absolute Deviation (MAD)2
Skewness0.43104057
Sum263478
Variance5.8189351
MonotonicityNot monotonic
2024-04-10T00:36:03.581938image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 12826
22.2%
6 10934
18.9%
1 7446
12.9%
5 5742
9.9%
2 5610
9.7%
3 5527
9.6%
7 3422
 
5.9%
10 3242
 
5.6%
8 1679
 
2.9%
9 1423
 
2.5%
ValueCountFrequency (%)
1 7446
12.9%
2 5610
9.7%
3 5527
9.6%
4 12826
22.2%
5 5742
9.9%
6 10934
18.9%
7 3422
 
5.9%
8 1679
 
2.9%
9 1423
 
2.5%
10 3242
 
5.6%
ValueCountFrequency (%)
10 3242
 
5.6%
9 1423
 
2.5%
8 1679
 
2.9%
7 3422
 
5.9%
6 10934
18.9%
5 5742
9.9%
4 12826
22.2%
3 5527
9.6%
2 5610
9.7%
1 7446
12.9%

Quantity
Real number (ℝ)

Distinct41
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5282709
Minimum1
Maximum44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:03.837965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile9
Maximum44
Range43
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0357664
Coefficient of variation (CV)0.86041193
Kurtosis14.057696
Mean3.5282709
Median Absolute Deviation (MAD)1
Skewness2.8354561
Sum204114
Variance9.2158775
MonotonicityNot monotonic
2024-04-10T00:36:04.096529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
1 13713
23.7%
2 13476
23.3%
3 9593
16.6%
4 7150
12.4%
5 4202
 
7.3%
6 3134
 
5.4%
7 1649
 
2.9%
8 1455
 
2.5%
9 798
 
1.4%
10 727
 
1.3%
Other values (31) 1954
 
3.4%
ValueCountFrequency (%)
1 13713
23.7%
2 13476
23.3%
3 9593
16.6%
4 7150
12.4%
5 4202
 
7.3%
6 3134
 
5.4%
7 1649
 
2.9%
8 1455
 
2.5%
9 798
 
1.4%
10 727
 
1.3%
ValueCountFrequency (%)
44 1
 
< 0.1%
41 1
 
< 0.1%
40 2
 
< 0.1%
39 1
 
< 0.1%
38 1
 
< 0.1%
36 2
 
< 0.1%
35 2
 
< 0.1%
34 3
< 0.1%
33 6
< 0.1%
32 7
< 0.1%

Unit Price
Real number (ℝ)

HIGH CORRELATION 

Distinct230
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446.38851
Minimum1.33
Maximum2146.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:04.368341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.33
5-th percentile11.665
Q134.93
median214.24
Q3672.29
95-th percentile1466.01
Maximum2146.96
Range2145.63
Interquartile range (IQR)637.36

Descriptive statistics

Standard deviation521.88016
Coefficient of variation (CV)1.1691165
Kurtosis1.304735
Mean446.38851
Median Absolute Deviation (MAD)197.97
Skewness1.4130702
Sum25824022
Variance272358.91
MonotonicityNot monotonic
2024-04-10T00:36:04.653582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
469.79 2969
 
5.1%
419.46 2198
 
3.8%
28.84 1593
 
2.8%
20.19 1493
 
2.6%
323.99 1463
 
2.5%
1466.01 1353
 
2.3%
1430.44 1251
 
2.2%
202.33 1203
 
2.1%
445.41 1165
 
2.0%
32.39 1095
 
1.9%
Other values (220) 42068
72.7%
ValueCountFrequency (%)
1.33 1
 
< 0.1%
1.37 150
0.3%
2.5 1
 
< 0.1%
2.74 19
 
< 0.1%
2.89 31
 
0.1%
2.99 351
0.6%
3.98 1
 
< 0.1%
4.32 1
 
< 0.1%
4.37 14
 
< 0.1%
4.5 3
 
< 0.1%
ValueCountFrequency (%)
2146.96 539
 
0.9%
2039.99 643
1.1%
2024.99 661
1.1%
1971.99 3
 
< 0.1%
1957.49 6
 
< 0.1%
1466.01 1353
2.3%
1430.44 1251
2.2%
1417.14 4
 
< 0.1%
1391.99 549
0.9%
1382.76 22
 
< 0.1%

Sales
Real number (ℝ)

HIGH CORRELATION 

Distinct1406
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1336.9383
Minimum1.37
Maximum30993.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:04.936820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.37
5-th percentile25.95
Q1127.8
median469.79
Q31488.54
95-th percentile5567.96
Maximum30993.04
Range30991.67
Interquartile range (IQR)1360.74

Descriptive statistics

Standard deviation2150.4083
Coefficient of variation (CV)1.6084574
Kurtosis16.847004
Mean1336.9383
Median Absolute Deviation (MAD)412.11
Skewness3.4113958
Sum77343217
Variance4624255.7
MonotonicityNot monotonic
2024-04-10T00:36:05.216226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
419.46 789
 
1.4%
939.58 665
 
1.1%
838.92 657
 
1.1%
469.79 639
 
1.1%
1409.37 596
 
1.0%
323.99 462
 
0.8%
445.41 431
 
0.7%
1430.44 411
 
0.7%
183.94 401
 
0.7%
647.98 374
 
0.6%
Other values (1396) 52426
90.6%
ValueCountFrequency (%)
1.37 25
 
< 0.1%
2.74 25
 
< 0.1%
2.99 29
 
0.1%
4.11 22
 
< 0.1%
4.77 35
 
0.1%
5.19 94
0.2%
5.39 79
0.1%
5.48 19
 
< 0.1%
5.7 21
 
< 0.1%
5.98 44
0.1%
ValueCountFrequency (%)
30993.04 1
 
< 0.1%
27607.86 1
 
< 0.1%
27536.04 1
 
< 0.1%
25515 1
 
< 0.1%
25447.37 1
 
< 0.1%
24913.56 1
 
< 0.1%
23663.88 2
< 0.1%
23602.32 2
< 0.1%
23489.88 3
< 0.1%
22291.08 1
 
< 0.1%

Cost
Real number (ℝ)

HIGH CORRELATION 

Distinct1430
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1323.2109
Minimum0.86
Maximum38530.39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:05.518421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.86
5-th percentile19.43
Q197.15
median461.44
Q31510.3
95-th percentile5537.34
Maximum38530.39
Range38529.53
Interquartile range (IQR)1413.15

Descriptive statistics

Standard deviation2169.7876
Coefficient of variation (CV)1.6397897
Kurtosis19.768571
Mean1323.2109
Median Absolute Deviation (MAD)422.18
Skewness3.5560074
Sum76549076
Variance4707978.4
MonotonicityNot monotonic
2024-04-10T00:36:05.827773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
413.15 789
 
1.4%
973.41 675
 
1.2%
826.29 655
 
1.1%
486.71 650
 
1.1%
1460.12 610
 
1.1%
461.44 597
 
1.0%
1481.94 481
 
0.8%
922.89 433
 
0.7%
2963.88 404
 
0.7%
1946.83 378
 
0.7%
Other values (1420) 52179
90.2%
ValueCountFrequency (%)
0.86 25
< 0.1%
1.71 25
< 0.1%
1.87 29
0.1%
2.57 22
< 0.1%
2.97 35
0.1%
3.36 51
0.1%
3.4 21
< 0.1%
3.43 19
 
< 0.1%
3.73 44
0.1%
4.28 17
 
< 0.1%
ValueCountFrequency (%)
38530.39 1
 
< 0.1%
32475.3 1
 
< 0.1%
31120.7 2
< 0.1%
28156.82 1
 
< 0.1%
26770.16 1
 
< 0.1%
26674.88 2
< 0.1%
25192.94 1
 
< 0.1%
24675.23 1
 
< 0.1%
23815.22 1
 
< 0.1%
23711.01 4
< 0.1%

Country
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size452.1 KiB
United States
37151 
Canada
10934 
France
 
3422
United Kingdom
 
3242
Germany
 
1679

Length

Max length14
Median length13
Mean length11.04643
Min length6

Characters and Unicode

Total characters639047
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States 37151
64.2%
Canada 10934
 
18.9%
France 3422
 
5.9%
United Kingdom 3242
 
5.6%
Germany 1679
 
2.9%
Australia 1423
 
2.5%

Length

2024-04-10T00:36:06.332268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T00:36:06.838695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
united 40393
41.1%
states 37151
37.8%
canada 10934
 
11.1%
france 3422
 
3.5%
kingdom 3242
 
3.3%
germany 1679
 
1.7%
australia 1423
 
1.4%

Most occurring characters

ValueCountFrequency (%)
t 116118
18.2%
e 82645
12.9%
a 77900
12.2%
n 59670
9.3%
d 54569
8.5%
i 45058
 
7.1%
U 40393
 
6.3%
40393
 
6.3%
s 38574
 
6.0%
S 37151
 
5.8%
Other values (13) 46576
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 639047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 116118
18.2%
e 82645
12.9%
a 77900
12.2%
n 59670
9.3%
d 54569
8.5%
i 45058
 
7.1%
U 40393
 
6.3%
40393
 
6.3%
s 38574
 
6.0%
S 37151
 
5.8%
Other values (13) 46576
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 639047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 116118
18.2%
e 82645
12.9%
a 77900
12.2%
n 59670
9.3%
d 54569
8.5%
i 45058
 
7.1%
U 40393
 
6.3%
40393
 
6.3%
s 38574
 
6.0%
S 37151
 
5.8%
Other values (13) 46576
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 639047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 116118
18.2%
e 82645
12.9%
a 77900
12.2%
n 59670
9.3%
d 54569
8.5%
i 45058
 
7.1%
U 40393
 
6.3%
40393
 
6.3%
s 38574
 
6.0%
S 37151
 
5.8%
Other values (13) 46576
7.3%

Continent
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size452.1 KiB
North America
48085 
Europe
8343 
Pacific
 
1423

Length

Max length13
Median length13
Mean length11.842907
Min length6

Characters and Unicode

Total characters685124
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth America
2nd rowNorth America
3rd rowNorth America
4th rowNorth America
5th rowNorth America

Common Values

ValueCountFrequency (%)
North America 48085
83.1%
Europe 8343
 
14.4%
Pacific 1423
 
2.5%

Length

2024-04-10T00:36:07.102450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T00:36:07.361004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
north 48085
45.4%
america 48085
45.4%
europe 8343
 
7.9%
pacific 1423
 
1.3%

Most occurring characters

ValueCountFrequency (%)
r 104513
15.3%
e 56428
8.2%
o 56428
8.2%
c 50931
7.4%
i 50931
7.4%
a 49508
 
7.2%
m 48085
 
7.0%
N 48085
 
7.0%
A 48085
 
7.0%
48085
 
7.0%
Other values (7) 124045
18.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 685124
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 104513
15.3%
e 56428
8.2%
o 56428
8.2%
c 50931
7.4%
i 50931
7.4%
a 49508
 
7.2%
m 48085
 
7.0%
N 48085
 
7.0%
A 48085
 
7.0%
48085
 
7.0%
Other values (7) 124045
18.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 685124
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 104513
15.3%
e 56428
8.2%
o 56428
8.2%
c 50931
7.4%
i 50931
7.4%
a 49508
 
7.2%
m 48085
 
7.0%
N 48085
 
7.0%
A 48085
 
7.0%
48085
 
7.0%
Other values (7) 124045
18.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 685124
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 104513
15.3%
e 56428
8.2%
o 56428
8.2%
c 50931
7.4%
i 50931
7.4%
a 49508
 
7.2%
m 48085
 
7.0%
N 48085
 
7.0%
A 48085
 
7.0%
48085
 
7.0%
Other values (7) 124045
18.1%
Distinct250
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size452.1 KiB
2024-04-10T00:36:07.768542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length32
Median length29
Mean length21.218492
Min length5

Characters and Unicode

Total characters1227511
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLong-Sleeve Logo Jersey, XL
2nd rowMountain-100 Black, 48
3rd rowMountain-100 Black, 38
4th rowLong-Sleeve Logo Jersey, L
5th rowHL Mountain Frame - Silver, 48
ValueCountFrequency (%)
black 12760
 
6.1%
11601
 
5.5%
mountain 8466
 
4.0%
frame 8331
 
4.0%
red 8037
 
3.8%
road 6574
 
3.1%
ll 6361
 
3.0%
yellow 6304
 
3.0%
hl 5868
 
2.8%
silver 5510
 
2.6%
Other values (90) 130713
62.1%
2024-04-10T00:36:08.524461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
152674
 
12.4%
e 79832
 
6.5%
a 73700
 
6.0%
o 72198
 
5.9%
l 55267
 
4.5%
0 50257
 
4.1%
- 49776
 
4.1%
n 49247
 
4.0%
, 46989
 
3.8%
r 40606
 
3.3%
Other values (47) 556965
45.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1227511
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
152674
 
12.4%
e 79832
 
6.5%
a 73700
 
6.0%
o 72198
 
5.9%
l 55267
 
4.5%
0 50257
 
4.1%
- 49776
 
4.1%
n 49247
 
4.0%
, 46989
 
3.8%
r 40606
 
3.3%
Other values (47) 556965
45.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1227511
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
152674
 
12.4%
e 79832
 
6.5%
a 73700
 
6.0%
o 72198
 
5.9%
l 55267
 
4.5%
0 50257
 
4.1%
- 49776
 
4.1%
n 49247
 
4.0%
, 46989
 
3.8%
r 40606
 
3.3%
Other values (47) 556965
45.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1227511
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
152674
 
12.4%
e 79832
 
6.5%
a 73700
 
6.0%
o 72198
 
5.9%
l 55267
 
4.5%
0 50257
 
4.1%
- 49776
 
4.1%
n 49247
 
4.0%
, 46989
 
3.8%
r 40606
 
3.3%
Other values (47) 556965
45.4%

Color
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)< 0.1%
Missing4853
Missing (%)8.4%
Memory size452.1 KiB
Black
19594 
Red
8037 
Yellow
7429 
Silver
6725 
Blue
4580 
Other values (3)
6633 

Length

Max length12
Median length6
Mean length5.0593607
Min length3

Characters and Unicode

Total characters268136
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMulti
2nd rowBlack
3rd rowBlack
4th rowMulti
5th rowSilver

Common Values

ValueCountFrequency (%)
Black 19594
33.9%
Red 8037
13.9%
Yellow 7429
 
12.8%
Silver 6725
 
11.6%
Blue 4580
 
7.9%
Multi 4447
 
7.7%
Silver/Black 1378
 
2.4%
White 808
 
1.4%
(Missing) 4853
 
8.4%

Length

2024-04-10T00:36:08.820968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T00:36:09.105092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
black 19594
37.0%
red 8037
15.2%
yellow 7429
 
14.0%
silver 6725
 
12.7%
blue 4580
 
8.6%
multi 4447
 
8.4%
silver/black 1378
 
2.6%
white 808
 
1.5%

Most occurring characters

ValueCountFrequency (%)
l 52960
19.8%
e 28957
10.8%
B 25552
9.5%
a 20972
 
7.8%
c 20972
 
7.8%
k 20972
 
7.8%
i 13358
 
5.0%
u 9027
 
3.4%
v 8103
 
3.0%
r 8103
 
3.0%
Other values (11) 59160
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 268136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 52960
19.8%
e 28957
10.8%
B 25552
9.5%
a 20972
 
7.8%
c 20972
 
7.8%
k 20972
 
7.8%
i 13358
 
5.0%
u 9027
 
3.4%
v 8103
 
3.0%
r 8103
 
3.0%
Other values (11) 59160
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 268136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 52960
19.8%
e 28957
10.8%
B 25552
9.5%
a 20972
 
7.8%
c 20972
 
7.8%
k 20972
 
7.8%
i 13358
 
5.0%
u 9027
 
3.4%
v 8103
 
3.0%
r 8103
 
3.0%
Other values (11) 59160
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 268136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 52960
19.8%
e 28957
10.8%
B 25552
9.5%
a 20972
 
7.8%
c 20972
 
7.8%
k 20972
 
7.8%
i 13358
 
5.0%
u 9027
 
3.4%
v 8103
 
3.0%
r 8103
 
3.0%
Other values (11) 59160
22.1%

Subcategory
Categorical

HIGH CORRELATION 

Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size452.1 KiB
Road Bikes
12559 
Mountain Bikes
7169 
Road Frames
4608 
Mountain Frames
4224 
Touring Bikes
3933 
Other values (28)
25358 

Length

Max length17
Median length14
Mean length10.056991
Min length4

Characters and Unicode

Total characters581807
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJerseys
2nd rowMountain Bikes
3rd rowMountain Bikes
4th rowJerseys
5th rowMountain Frames

Common Values

ValueCountFrequency (%)
Road Bikes 12559
21.7%
Mountain Bikes 7169
12.4%
Road Frames 4608
 
8.0%
Mountain Frames 4224
 
7.3%
Touring Bikes 3933
 
6.8%
Jerseys 3667
 
6.3%
Helmets 2640
 
4.6%
Gloves 2105
 
3.6%
Wheels 1899
 
3.3%
Shorts 1493
 
2.6%
Other values (23) 13554
23.4%

Length

2024-04-10T00:36:09.408396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bikes 23661
25.2%
road 17167
18.3%
mountain 11393
12.2%
frames 10092
10.8%
touring 5193
 
5.5%
jerseys 3667
 
3.9%
helmets 2640
 
2.8%
gloves 2105
 
2.2%
wheels 1899
 
2.0%
shorts 1493
 
1.6%
Other values (28) 14442
15.4%

Most occurring characters

ValueCountFrequency (%)
s 63503
 
10.9%
e 61171
 
10.5%
a 49047
 
8.4%
i 43423
 
7.5%
o 40713
 
7.0%
35901
 
6.2%
n 31255
 
5.4%
k 27089
 
4.7%
B 26196
 
4.5%
r 25400
 
4.4%
Other values (27) 178109
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 63503
 
10.9%
e 61171
 
10.5%
a 49047
 
8.4%
i 43423
 
7.5%
o 40713
 
7.0%
35901
 
6.2%
n 31255
 
5.4%
k 27089
 
4.7%
B 26196
 
4.5%
r 25400
 
4.4%
Other values (27) 178109
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 63503
 
10.9%
e 61171
 
10.5%
a 49047
 
8.4%
i 43423
 
7.5%
o 40713
 
7.0%
35901
 
6.2%
n 31255
 
5.4%
k 27089
 
4.7%
B 26196
 
4.5%
r 25400
 
4.4%
Other values (27) 178109
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 63503
 
10.9%
e 61171
 
10.5%
a 49047
 
8.4%
i 43423
 
7.5%
o 40713
 
7.0%
35901
 
6.2%
n 31255
 
5.4%
k 27089
 
4.7%
B 26196
 
4.5%
r 25400
 
4.4%
Other values (27) 178109
30.6%

Category
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size452.1 KiB
Bikes
23661 
Components
17619 
Clothing
11745 
Accessories
4826 

Length

Max length11
Median length10
Mean length7.6323832
Min length5

Characters and Unicode

Total characters441541
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClothing
2nd rowBikes
3rd rowBikes
4th rowClothing
5th rowComponents

Common Values

ValueCountFrequency (%)
Bikes 23661
40.9%
Components 17619
30.5%
Clothing 11745
20.3%
Accessories 4826
 
8.3%

Length

2024-04-10T00:36:09.675745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-10T00:36:09.957575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bikes 23661
40.9%
components 17619
30.5%
clothing 11745
20.3%
accessories 4826
 
8.3%

Most occurring characters

ValueCountFrequency (%)
s 55758
12.6%
o 51809
11.7%
e 50932
11.5%
n 46983
10.6%
i 40232
9.1%
C 29364
6.7%
t 29364
6.7%
B 23661
 
5.4%
k 23661
 
5.4%
p 17619
 
4.0%
Other values (7) 72158
16.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 441541
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 55758
12.6%
o 51809
11.7%
e 50932
11.5%
n 46983
10.6%
i 40232
9.1%
C 29364
6.7%
t 29364
6.7%
B 23661
 
5.4%
k 23661
 
5.4%
p 17619
 
4.0%
Other values (7) 72158
16.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 441541
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 55758
12.6%
o 51809
11.7%
e 50932
11.5%
n 46983
10.6%
i 40232
9.1%
C 29364
6.7%
t 29364
6.7%
B 23661
 
5.4%
k 23661
 
5.4%
p 17619
 
4.0%
Other values (7) 72158
16.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 441541
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 55758
12.6%
o 51809
11.7%
e 50932
11.5%
n 46983
10.6%
i 40232
9.1%
C 29364
6.7%
t 29364
6.7%
B 23661
 
5.4%
k 23661
 
5.4%
p 17619
 
4.0%
Other values (7) 72158
16.3%

Profit
Real number (ℝ)

HIGH CORRELATION 

Distinct1545
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.727361
Minimum-16444.39
Maximum1278.36
Zeros0
Zeros (%)0.0%
Negative20599
Negative (%)35.6%
Memory size452.1 KiB
2024-04-10T00:36:10.208790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-16444.39
5-th percentile-169.17
Q1-18.36
median13.84
Q358.56
95-th percentile296.25
Maximum1278.36
Range17722.75
Interquartile range (IQR)76.92

Descriptive statistics

Standard deviation391.19874
Coefficient of variation (CV)28.497738
Kurtosis533.66001
Mean13.727361
Median Absolute Deviation (MAD)38.17
Skewness-18.22807
Sum794141.57
Variance153036.46
MonotonicityNot monotonic
2024-04-10T00:36:10.487337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.31 851
 
1.5%
-33.83 665
 
1.1%
12.63 655
 
1.1%
-16.92 639
 
1.1%
-50.75 596
 
1.0%
-16.03 431
 
0.7%
-51.5 411
 
0.7%
-67.67 369
 
0.6%
-103 342
 
0.6%
18.94 340
 
0.6%
Other values (1535) 52552
90.8%
ValueCountFrequency (%)
-16444.39 2
 
< 0.1%
-15229.24 1
 
< 0.1%
-14509.76 5
< 0.1%
-12790.08 2
 
< 0.1%
-11597.79 1
 
< 0.1%
-11421.93 3
< 0.1%
-11094.47 1
 
< 0.1%
-10543.44 1
 
< 0.1%
-9489.1 1
 
< 0.1%
-9135.77 6
< 0.1%
ValueCountFrequency (%)
1278.36 4
 
< 0.1%
1268.96 4
 
< 0.1%
1263.7 7
< 0.1%
1250.09 8
< 0.1%
1249.94 7
< 0.1%
1236.5 9
< 0.1%
1157.38 2
 
< 0.1%
1150.52 3
 
< 0.1%
1142.06 11
< 0.1%
1137.33 12
< 0.1%

Profit_Margin
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1219
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.8897676
Minimum-22395.941
Maximum40.438596
Zeros0
Zeros (%)0.0%
Negative20599
Negative (%)35.6%
Memory size452.1 KiB
2024-04-10T00:36:10.811270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-22395.941
5-th percentile-28.348256
Q1-3.5989313
median7.6467229
Q325.998079
95-th percentile37.65976
Maximum40.438596
Range22436.38
Interquartile range (IQR)29.597011

Descriptive statistics

Standard deviation555.18983
Coefficient of variation (CV)-94.263454
Kurtosis1598.7284
Mean-5.8897676
Median Absolute Deviation (MAD)11.247623
Skewness-39.796014
Sum-340728.94
Variance308235.75
MonotonicityNot monotonic
2024-04-10T00:36:11.104192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.600899693 809
 
1.4%
1.504315072 791
 
1.4%
-3.60029082 753
 
1.3%
10.05726091 750
 
1.3%
-3.600544924 665
 
1.1%
1.505507081 655
 
1.1%
-3.60160923 639
 
1.1%
-1.133986442 601
 
1.0%
-0.8979442242 563
 
1.0%
-6.067322138 549
 
0.9%
Other values (1209) 51076
88.3%
ValueCountFrequency (%)
-22395.94118 35
0.1%
-7378.928571 2
 
< 0.1%
-2627.59292 3
 
< 0.1%
-2149.594074 5
 
< 0.1%
-2149.593464 2
 
< 0.1%
-2149.593277 2
 
< 0.1%
-2149.592941 6
 
< 0.1%
-2149.592157 10
 
< 0.1%
-724.8952381 1
 
< 0.1%
-648 2
 
< 0.1%
ValueCountFrequency (%)
40.43859649 49
0.1%
40.43255737 107
0.2%
40.43019883 17
 
< 0.1%
40.42842992 94
0.2%
40.4270541 5
 
< 0.1%
40.42606516 4
 
< 0.1%
40.42595344 54
0.1%
40.42430246 23
 
< 0.1%
40.42105263 20
 
< 0.1%
40.41666667 9
 
< 0.1%

trimestre
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size452.1 KiB

Interactions

2024-04-10T00:35:54.352190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:28.033199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:30.723410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:33.573529image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:36.825516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:39.352123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:42.094752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:45.077960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:48.654038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:51.758581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:54.624199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:28.307532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:31.006367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:33.932109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:37.101963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:39.819993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:42.356216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:45.581552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:49.094079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:52.037442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:54.878197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:28.588631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:31.274200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:34.276904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:37.366377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:40.096992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:42.616167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:45.840363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:49.375920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:52.305159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:55.137080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:28.840014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:31.543662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:34.622944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:37.611551image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:40.339279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:42.853112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:46.102493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:49.629167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:52.553410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:55.383656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:29.094138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:31.959492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:34.971404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:37.854111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:40.570285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:43.108844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:46.426137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:49.891858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:52.801586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:55.631533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:29.362943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:32.201007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:35.319483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:38.080954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:40.820696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:43.349223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:46.809575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:50.408246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:53.052986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:55.861544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:29.632953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:32.452514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:35.628562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:38.331807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:41.070023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:43.572094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:47.161825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:50.659965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:53.300715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:56.119351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:29.904693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:32.727156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:35.996906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:38.588095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:41.320673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:43.821170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:47.546171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:50.923955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:53.567744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:56.381639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:30.183794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:32.997914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:36.324673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:38.862362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:41.586489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:44.483667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:47.914371image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:51.212808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:53.837784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:56.649077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:30.471096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:33.260675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:36.568530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:39.112320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:41.832625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:44.736592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:48.316283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:51.509185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-04-10T00:35:54.093954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-04-10T00:36:11.344992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
CategoryColorContinentCostCountryEmployeeKeyProductKeyProfitProfit_MarginQuantityResellerKeySalesSalesTerritoryKeySubcategoryUnit Price
Category1.0000.4380.038-0.2760.0410.0080.0940.2360.142-0.1060.013-0.269-0.0001.000-0.233
Color0.4381.0000.1750.1020.1200.0410.159-0.252-0.277-0.043-0.0010.0730.0320.7870.101
Continent0.0380.1751.0000.0241.000-0.319-0.0720.004-0.023-0.065-0.0360.022-0.4570.2360.048
Cost-0.2760.1020.0241.0000.016-0.021-0.019-0.108-0.5410.160-0.0060.991-0.0400.1260.912
Country0.0410.1201.0000.0161.000-0.618-0.0920.030-0.006-0.027-0.0210.034-0.7860.1620.043
EmployeeKey0.0080.041-0.319-0.021-0.6181.0000.146-0.044-0.0040.0180.039-0.0230.5000.138-0.026
ProductKey0.0940.159-0.072-0.019-0.0920.1461.000-0.0690.067-0.0400.023-0.0090.0940.7470.004
Profit0.236-0.2520.004-0.1080.030-0.044-0.0691.0000.7000.1340.030-0.040-0.0500.079-0.090
Profit_Margin0.142-0.277-0.023-0.541-0.006-0.0040.0670.7001.0000.1000.015-0.465-0.0030.038-0.484
Quantity-0.106-0.043-0.0650.160-0.0270.018-0.0400.1340.1001.000-0.0220.1770.0200.116-0.207
ResellerKey0.013-0.001-0.036-0.006-0.0210.0390.0230.0300.015-0.0221.000-0.007-0.0010.0590.006
Sales-0.2690.0730.0220.9910.034-0.023-0.009-0.040-0.4650.177-0.0071.000-0.0420.1510.908
SalesTerritoryKey-0.0000.032-0.457-0.040-0.7860.5000.094-0.050-0.0030.020-0.001-0.0421.0000.133-0.048
Subcategory1.0000.7870.2360.1260.1620.1380.7470.0790.0380.1160.0590.1510.1331.0000.344
Unit Price-0.2330.1010.0480.9120.043-0.0260.004-0.090-0.484-0.2070.0060.908-0.0480.3441.000

Missing values

2024-04-10T00:35:57.057527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-10T00:35:57.785192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SalesOrderNumberOrderDateProductKeyResellerKeyEmployeeKeySalesTerritoryKeyQuantityUnit PriceSalesCostCountryContinentProductColorSubcategoryCategoryProfitProfit_Margintrimestre
0SO438972017-08-252353122824228.8457.6863.45United StatesNorth AmericaLong-Sleeve Logo Jersey, XLMultiJerseysClothing-5.77-10.0034672017-08-25 00:00
1SO438972017-08-25351312282422024.994049.983796.19United StatesNorth AmericaMountain-100 Black, 48BlackMountain BikesBikes253.796.2664512017-08-25 00:00
2SO438972017-08-25348312282422024.994049.983796.19United StatesNorth AmericaMountain-100 Black, 38BlackMountain BikesBikes253.796.2664512017-08-25 00:00
3SO438972017-08-252323122824228.8457.6863.45United StatesNorth AmericaLong-Sleeve Logo Jersey, LMultiJerseysClothing-5.77-10.0034672017-08-25 00:00
4SO445442017-11-1829231228242818.701637.401413.62United StatesNorth AmericaHL Mountain Frame - Silver, 48SilverMountain FramesComponents223.7813.6667892017-11-18 00:00
5SO445442017-11-182203122824220.1940.3824.06United StatesNorth AmericaSport-100 Helmet, BlueBlueHelmetsAccessories16.3240.4160482017-11-18 00:00
6SO445442017-11-18351312282422024.994049.983796.19United StatesNorth AmericaMountain-100 Black, 48BlackMountain BikesBikes253.796.2664512017-11-18 00:00
7SO445442017-11-18349312282422024.994049.983796.19United StatesNorth AmericaMountain-100 Black, 42BlackMountain BikesBikes253.796.2664512017-11-18 00:00
8SO445442017-11-18344312282422039.994079.983824.31United StatesNorth AmericaMountain-100 Silver, 38SilverMountain BikesBikes255.676.2664522017-11-18 00:00
9SO453212018-02-18346312282422039.994079.983824.31United StatesNorth AmericaMountain-100 Silver, 44SilverMountain BikesBikes255.676.2664522018-02-18 00:00
SalesOrderNumberOrderDateProductKeyResellerKeyEmployeeKeySalesTerritoryKeyQuantityUnit PriceSalesCostCountryContinentProductColorSubcategoryCategoryProfitProfit_Margintrimestre
57841SO673492020-04-304718128552034.93698.60474.98United StatesNorth AmericaClassic Vest, SBlueVestsClothing223.6232.0097342020-04-30 00:00
57842SO694162020-05-064823772833204.9498.8067.25United StatesNorth AmericaRacing Socks, LWhiteSocksClothing31.5531.9331982020-05-06 00:00
57843SO694242020-05-074823102813284.50126.0094.14United StatesNorth AmericaRacing Socks, LWhiteSocksClothing31.8625.2857142020-05-07 00:00
57844SO694372020-05-0947117529272434.93838.32569.98FranceEuropeClassic Vest, SBlueVestsClothing268.3432.0092572020-05-09 00:00
57845SO694712020-05-1447669728212038.49769.80523.53United StatesNorth AmericaWomen's Mountain Shorts, LBlackShortsClothing246.2731.9914262020-05-14 00:00
57846SO694762020-05-1547666728322138.49808.29549.70United StatesNorth AmericaWomen's Mountain Shorts, LBlackShortsClothing258.5931.9922312020-05-15 00:00
57847SO694762020-05-1547466728321938.49731.31497.35United StatesNorth AmericaWomen's Mountain Shorts, SBlackShortsClothing233.9631.9919052020-05-15 00:00
57848SO694932020-05-18482892832204.9498.8067.25United StatesNorth AmericaRacing Socks, LWhiteSocksClothing31.5531.9331982020-05-18 00:00
57849SO695032020-05-20482792823234.94113.6277.33United StatesNorth AmericaRacing Socks, LWhiteSocksClothing36.2931.9397992020-05-20 00:00
57850SO695612020-05-3147454628232635.00910.00680.58United StatesNorth AmericaWomen's Mountain Shorts, SBlackShortsClothing229.4225.2109892020-05-31 00:00